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Record W1987078379 · doi:10.1145/2799250.2799282

Smartwatches vs. smartphones

2015· article· en· W1987078379 on OpenAlex
Wayne C.W. Giang, Inas Shanti, Huei-Yen Winnie Chen, Alex Zhou, Birsen Donmez

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPsychology
TopicHuman-Automation Interaction and Safety
Canadian institutionsUniversity of Toronto
FundersLam Research
KeywordsSmartwatchComputer scienceDisconnectionHuman–computer interactionInternet privacyComputer securityWearable computerEmbedded system

Abstract

fetched live from OpenAlex

This study examines driver engagement with smartwatches and smartphones while driving. Twelve participants (7 novice and 5 experienced smartwatch users) drove in a high-fidelity simulator while receiving notifications from either a smartwatch (Pebble) or a smartphone (LG Nexus 5). It was found that participants had more glances, on average, per notification while using the smartwatch compared to the smartphone. Further, their brake response times were longer when they received notifications prior to a lead vehicle braking event on the smartwatch compared to when they did not receive any notifications and when they received notifications on the smartphone. Contrary to these glance and driving performance findings, participants perceived similar levels of risk for the two devices, and they largely reported that smartwatch use while driving should receive penalties equal to or less than smartphone use with respect to distracted driving legislation. Thus, there appears to be a disconnection between drivers' actual performance while using smartwatches and their perceptions.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.722
Threshold uncertainty score0.975

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0370.026

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.084
GPT teacher head0.385
Teacher spread0.301 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Quick stats

Citations29
Published2015
Admission routes1
Has abstractyes

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